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RNAspa: a shortest path approach for comparative prediction of the secondary structure of ncRNA molecules.

Horesh Y, Doniger T, Michaeli S, Unger R - BMC Bioinformatics (2007)

Bottom Line: We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy.These datasets allowed for comparison of the algorithm with other methods.In these tests, RNAspa performed better than four other programs.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel. yair@biomodel.os.biu.ac.il

ABSTRACT

Background: In recent years, RNA molecules that are not translated into proteins (ncRNAs) have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure.

Results: We developed the RNAspa program, which comparatively predicts the secondary structure for a set of ncRNA molecules in linear time in the number of molecules. We observed that in a list of several hundred suboptimal minimal free energy (MFE) predictions, as provided by the RNAsubopt program of the Vienna package, it is likely that at least one suggested structure would be similar to the true, correct one. The suboptimal solutions of each molecule are represented as a layer of vertices in a graph. The shortest path in this graph is the basis for structural predictions for the molecule. We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy. We show that this approach allows us to more deeply explore the suboptimal structure space.

Conclusion: The algorithm was tested on three datasets which include several ncRNA families taken from the Rfam database. These datasets allowed for comparison of the algorithm with other methods. In these tests, RNAspa performed better than four other programs.

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The influence of the number of suboptimal structures on accuracy. The performance of the algorithm as a function of the number of suboptimal predictions used as input for our eight datasets. For most datasets, the results improve substantially for the first 50–150 predictions. A further increase in the number of suboptimal structures does not yield much better accuracy. Hence, a value of 150 predictions was chosen as the default for the RNAspa program.
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Figure 7: The influence of the number of suboptimal structures on accuracy. The performance of the algorithm as a function of the number of suboptimal predictions used as input for our eight datasets. For most datasets, the results improve substantially for the first 50–150 predictions. A further increase in the number of suboptimal structures does not yield much better accuracy. Hence, a value of 150 predictions was chosen as the default for the RNAspa program.

Mentions: We next measured the effect of our major parameter, the number of suboptimal solutions considered for each sequence, on the performance of RNAspa. Results are shown in Figure 7. As expected, a larger set of suboptimal solutions tested yields better accuracy. However, it seems that about 150 vertices are sufficient, as the accuracy level doesn't increase substantially if the number of vertices is further increased.


RNAspa: a shortest path approach for comparative prediction of the secondary structure of ncRNA molecules.

Horesh Y, Doniger T, Michaeli S, Unger R - BMC Bioinformatics (2007)

The influence of the number of suboptimal structures on accuracy. The performance of the algorithm as a function of the number of suboptimal predictions used as input for our eight datasets. For most datasets, the results improve substantially for the first 50–150 predictions. A further increase in the number of suboptimal structures does not yield much better accuracy. Hence, a value of 150 predictions was chosen as the default for the RNAspa program.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2147038&req=5

Figure 7: The influence of the number of suboptimal structures on accuracy. The performance of the algorithm as a function of the number of suboptimal predictions used as input for our eight datasets. For most datasets, the results improve substantially for the first 50–150 predictions. A further increase in the number of suboptimal structures does not yield much better accuracy. Hence, a value of 150 predictions was chosen as the default for the RNAspa program.
Mentions: We next measured the effect of our major parameter, the number of suboptimal solutions considered for each sequence, on the performance of RNAspa. Results are shown in Figure 7. As expected, a larger set of suboptimal solutions tested yields better accuracy. However, it seems that about 150 vertices are sufficient, as the accuracy level doesn't increase substantially if the number of vertices is further increased.

Bottom Line: We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy.These datasets allowed for comparison of the algorithm with other methods.In these tests, RNAspa performed better than four other programs.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel. yair@biomodel.os.biu.ac.il

ABSTRACT

Background: In recent years, RNA molecules that are not translated into proteins (ncRNAs) have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure.

Results: We developed the RNAspa program, which comparatively predicts the secondary structure for a set of ncRNA molecules in linear time in the number of molecules. We observed that in a list of several hundred suboptimal minimal free energy (MFE) predictions, as provided by the RNAsubopt program of the Vienna package, it is likely that at least one suggested structure would be similar to the true, correct one. The suboptimal solutions of each molecule are represented as a layer of vertices in a graph. The shortest path in this graph is the basis for structural predictions for the molecule. We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy. We show that this approach allows us to more deeply explore the suboptimal structure space.

Conclusion: The algorithm was tested on three datasets which include several ncRNA families taken from the Rfam database. These datasets allowed for comparison of the algorithm with other methods. In these tests, RNAspa performed better than four other programs.

Show MeSH
Related in: MedlinePlus